We aim to research, understand and develop technologies that are centred around e-Health.
With the rapid development of communication networks and distributed systems, it is now possible to use video audio, and scalar sensors for structural health monitoring (SHM) for reducing the costs associated with and improving the health related services. Furthermore, it is possible to obtain accurate and detailed information, for example, in application areas such as the analysis of behaviour or rehabilitation of elderly people and people with various types of disabilities. The available video and audio sensors along with some of the traditional scalar sensors are effectively used in identifying emergency situations more accurately in real-time as well.
We have a variety of research projects that range from sensing the environment and making decisions on users’ behaviour/activities to applications that are targeting in understanding users health conditions automatically. In brief, we have the following two lines of research projects.
- We mainly investigate different techniques to understand users’ abilities and develop technologies accordingly. For instance, we are exploring how to interpret data collected by eye trackers to identify the difficulties people with autism experience. This research aims to not only understand the differences but also use these differences in automatic detection of autism.
- We also have a line of research that aims to collect data about users’ behaviours and activities via different sensors, and then develop algorithms and techniques to analyse, interpret and understand the users’ behaviour and activities automatically. These would then be used to improve either user's health conditions, surroundings or make their environment smarter. Therefore, the overall goal here is to create smart environments by sensing the environment and acting open smartly.
Selected Related Publications:
- Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva and Le An Ha. 2020. "Keep it Simple!" An Eye-tracking Study for Exploring Complexity and Distinguishability of Web Pages for People with Autism. Universal Access in the Information Society.
- Sukru Eraslan, Victoria Yaneva, Yeliz Yesilada and Simon Harper. 2019. Web users with autism: eye tracking evidence for differences, Behaviour & Information Technology, 38, 7, 678-700.
- Hakan Yekta Yatbaz, Sukru Eraslan, Yeliz Yesilada and Enver Ever. 2019. Activity Recognition Using Binary Sensors for Elderly People Living Alone: Scanpath Trend Analysis Approach. IEEE Sensors Journal, (SCI-E), 19 , 17, 7575-7582.
- Oludamilare Matthews, Sukru Eraslan, Victoria Yaneva, Alan Davies, Yeliz Yesilada, Markel Vigo, and Simon Harper. 2019. Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web: A Case Study of Neurotypical People vs People with Autism. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '19). ACM, New York, NY, USA, 86-94.
- Victoria Yaneva, Le An Ha, Sukru Eraslan, and Yeliz Yesilada. 2019. Adults with High-functioning Autism Process Web Pages With Similar Accuracy but Higher Cognitive Effort Compared to Controls. In Proceedings of the 16th Web For All 2019 Personalization - Personalizing the Web (W4A '19). ACM, New York, NY, USA, Article 34, 4 pages.
- Victoria Yaneva, Le An Ha, Sukru Eraslan and Yeliz Yesilada. 2018. Autism and the Web: Using Web-searching Tasks to Detect Autism and Improve Web Accessibility. ACM SIGACCESS Newsletter, Hugo Nicolau (Ed.), Issue 121 June 2018, Article 2.
- Victoria Yaneva, Le An Ha, Sukru Eraslan, Yeliz Yesilada, and Ruslan Mitkov. 2018. Detecting Autism Based on Eye-Tracking Data from Web Searching Tasks. In Proceedings of the Internet of Accessible Things (W4A '18). ACM, New York, NY, USA, Article 16, 10 pages.
- Sukru Eraslan, Victoria Yaneva, Yeliz Yesilada, and Simon Harper. 2017. Do Web Users with Autism Experience Barriers When Searching for Information Within Web Pages?. In Proceedings of the 14th Web for All Conference on The Future of Accessible Work (W4A '17). ACM, New York, NY, USA, Article 20, 4 pages.